Enabling interoperability between participants in a network

ABSTRACT

Interoperability is enabled between participants in a network by determining values associated with a value metric defined for at least a portion of the network. Information flow is directed between two or more of the participants based at least in part on semantic models corresponding to the participants and on the values associated with the value metric. The semantic models may define interactions between the participants and define at least a portion of information produced or consumed by the participants. The determination of the values and the direction of the information flow may be performed multiple times in order to modify the one or more value metrics. The direction of information flow may allow participants to be deleted from the network, may allow participants to be added to the network, or may allow behavior of the participants to be modified.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/834,767, filed Apr. 29, 2004 (now abandoned), incorporated byreference herein.

FIELD OF THE INVENTION

The present invention relates to networking and, more particularly,relates to interactions between participants in a network.

BACKGROUND OF THE INVENTION

By way of example, participants in a network, such as the Internet, canbe producers, consumers, and transducers. A producer createsinformation. As an illustration of a producer, a pharmacy could outputto the network the types of Over-The-Counter (OTC) medication purchasedduring a certain time period. A consumer uses information. Thus, aconsumer could be a program that can schedule refills of particular OTCmedications when inventory reported by the pharmacy meets apredetermined low level. A transducer transforms the information andcreates transformed information. Consequently, a transducer couldexamine the numbers of remaining bottles of particular OTC medicationsfor certain ailments, such as colds, and determine that one OTCmedication is more preferable than another. The transformed informationcan then be made available to a consumer or another transducer. Forinstance, a consumer could use this information to schedule morefrequent refills of the more preferred OTC medication.

Although networks having participants are valuable, there is a need forproviding effective interoperability between the participants innetworks.

SUMMARY OF THE INVENTION

Principles of exemplary embodiments of the present invention provideinteroperability between participants in a network.

In an aspect of the invention, interoperability is enabled between aplurality of participants in a network by determining one or more valuesassociated with a value metric defined for at least a portion of thenetwork. Additionally, information flow is directed between two or moreof the plurality of participants in the network based at least in parton one or more semantic models corresponding to the plurality ofparticipants and on the one or more values associated with the valuemetric.

The one or more semantic models may define interactions between theplurality of participants and define at least a portion of informationproduced or consumed by the plurality of participants.

The determination of the one or more values and the direction of theinformation flow may be performed multiple times in order to modify theone or more value metrics. The direction of information flow may allowparticipants to be deleted from the network, may allow participants tobe added to the network, or may allow behavior of the participants to bemodified.

The semantic models may be externalized so that all participants canview the models, and the externalization can involve specificallydesigned ontologies or federated ontologies or both. Low level metricsmay be used when the one or more values of the value metric aredetermined. The value metric can be simple (e.g., an equation thatevaluates to a value) or complex (e.g., a rule).

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following detailed description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a network suitable for enablinginteroperability between producers, consumers, and transducers inaccordance with an exemplary embodiment of the present invention;

FIG. 2 is an exemplary block diagram of a producer in accordance with anexemplary embodiment of the present invention;

FIG. 3 is an exemplary block diagram illustrating how a transducer mightbe used as an information source, in accordance with an exemplaryembodiment of the present invention;

FIG. 4 is an exemplary block diagram illustrating a consumer andpossible interactions thereof, in accordance with an exemplaryembodiment of the present invention;

FIG. 5 is a block diagram illustrating three possible producers in anexemplary health monitoring scenario, in accordance with an exemplaryembodiment of the present invention;

FIG. 6 is a block diagram illustrating, for an exemplary healthmonitoring scenario, a consumer that is an anomaly analyzer using twosets of information inputs from two different producers to produce anearly warning alarm level for outbreak of a particular disease, inaccordance with an exemplary embodiment of the present invention;

FIG. 7 is a block diagram illustrating a transducer, for an exemplaryhealth monitoring scenario, that produces normalized aggregatedage-based and population flow based drug sales information, inaccordance with an exemplary embodiment of the present invention;

FIG. 8 is a block diagram of an exemplary semantic matchmaking device,in accordance with an exemplary embodiment of the present invention;

FIG. 9 is a block diagram illustrating adaptation of a consumer tooptimize the contribution of the consumer to a total value of a network,in accordance with an exemplary embodiment of the present invention;

FIG. 10 is an exemplary block diagram illustrating a matchmakinganalyzer discovering a relationship between multiple participantsthrough analysis of a semantic directory corresponding to theparticipants and based on the performance of the participants, asmeasured by performance metrics, in accordance with an exemplaryembodiment of the present invention;

FIG. 11 is an exemplary block diagram of a snapshot of adaptivematchmaking of producers and consumers at a particular state in time,where dashed connections represent dynamic linkages between producersand consumers, in accordance with an exemplary embodiment of the presentinvention;

FIG. 12 is a block diagram of the adaptive matchmaking of FIG. 11 at adifferent state in time, when producer 2 has been deleted from thenetwork, in accordance with an exemplary embodiment of the presentinvention;

FIG. 13 is a block diagram of the adaptive matchmaking of FIG. 12 atanother state in time, when a new producer has been added to thenetwork, in accordance with an exemplary embodiment of the presentinvention; and

FIG. 14 is a block diagram of a computer system suitable for use withembodiments of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

There are certain conventional systems that attempt to provideinteroperability. For instance, autonomous management of computinginfrastructure is known and provides for self-configuring, self-healing,self-optimizing and self-protecting computing infrastructure. Also knownis interoperability amongst low level web services such as the WebServices Description Language (WSDL), as described in Web ServicesDescription Language (WSDL) 1.1 of the World Wide Web Consortium (W3C),W3C Note (March 2001), the disclosure of which is hereby incorporated byreference.

Similarly, there is also a high-level semantic interoperability for webservices such as the Semantic Web, as described in J. Handler, TBerners-Lee and E. Miller, “Integrating Applications on the SemanticWeb,” Journal of the Institute of Electrical Engineers of Japan, Vol122(10), 676-680 (October 2002), the disclosure of which is herebyincorporated by reference. The main idea of the Semantic Web is toextend the current World Wide Web so that information is givenwell-defined meaning, thus better enabling computers and people to workin cooperation. The idea is to have data on the Web defined and linkedsuch that the data can be used for more effective discovery, automation,integration and reuse across various applications. The aim is to providean infrastructure that enables services, sensors, programs andappliances to both consume and produce data on the web.

While the Semantic Web is still more of a goal than an operationalinfrastructure, several of its components now exist. The components thathave emerged include the linking of databases, sharing content amongdifferent applications using different eXtensible Markup Language (XML)Document Type Definitions (DTDs) or schemas, and the emerging trendstowards Service Oriented Architecture (SOA)-based applications andsolutions that incorporate discovery and composition of web services.However, capability beyond that offered by XML-schema is needed toprovide mapping capabilities between divergent schemas corresponding todifferent databases. To this end, a fundamental component of theSemantic Web is the Resource Descriptor Framework. The other relatedcomponent of the Semantic Web that is related to our invention is the“Semantic Web Services,” which brings programs and data together. Newprotocols and languages are being developed rapidly to standardize theways in which systems describe what they do. An XML-based protocolcalled Simple Object Access Protocol (SOAP) has been developed toprovide standard means for allowing programs to invoke other programs onthe Web. In addition, new web service description and web servicelanguages are emerging. While predefined service definitions may beeasier to handle, discovering new services that use differentdescriptions may not be possible without newly emerging resources suchas the web ontology language.

While conventional systems have benefits, there is no existing systemthat allows interoperability between participants in a network to createa value network. Illustratively, a “value network” is a network that hasone or more value metrics associated with the network. The one or morevalue metrics can be used to improve values associated with the network.The participants of such a value network may adapt from time to time.Additionally, certain components in the value network are adapted tomaximize the value, as determined by a value metric, delivered by thenetwork.

In the case of the Semantic Web, for instance, there is no precisenotion of “value” as a result of bringing consumers and producers ofinformation together. The “value” created in the Semantic Web is theenabling of services to consume the data. Thus, there is a need for asystem that can enable the creation and autonomous maintenance of valuenetworks.

Exemplary embodiments of the present invention can create such a system.One difference between certain embodiments of the present invention andthe Semantic Web is that embodiments of the present invention candeliver a network whose value can be modified, such as throughoptimization, based on matchmaking between the components. In anexemplary embodiment, matchmaking for components directs informationflow between components in order to modify a value metric associatedwith the network and based on semantics provided for the components. Forinstance, two components will be connected (e.g. through informationflow) if semantics indicate the components can be connected and if thenetwork determines that the value metric is (or might be) modified bythe connection. The two components are matched when semantics indicatethe components can be connected and when the network determines that thevalue metric is modified by the connection. Once a match is determined,the information flow is directed between the two components. Exemplaryembodiments of the present invention allow the interoperability betweennetwork participants to be automatic and dynamic.

The following description uses a health monitoring scenario, butembodiments of the present invention are capable of enablinginteroperability for value networks for other reasons and are notconstrained by the particular application or by the kind of value metricused when enabling interoperability.

FIG. 1 shows a block diagram of a value network 100. Value network 100can also be considered to be an exemplary framework to enableinteroperability of participants in a network. Illustratively, valuenetwork 100 can be implemented in a health monitoring scenario by, forexample, the Center for Disease Control (CDC), a local healthorganization, a producer of medications, or a conglomerate of producersof medications, pharmacies, and health organizations. Value network 100can be implemented by any organization or group of organizations wishingto define a value metric for a network and to modify the value metric.

Participants are specific types of components that can be used toconstruct a value network, such as value network 100. Value network 100includes the participants of producers 105-1 and 105-2, the consumers110-1 and 110-2, and the transducers 115-1 through 115-3. A semanticmatchmaking device 135 is coupled to the producers 105-1 and 105-2, theconsumers 110-1 and 110-2, and the transducers 115-1 through 115-3, to avalue definition device 120, to storage 130, and to a semantic directory125.

In an exemplary embodiment, the semantic matchmaking device 135dynamically aligns various network components and allocates resources tooptimize a value metric, as defined by the value definition device 120,for which the value network 100 is assembled. Typically, all componentsof the value network 100 have inputs and outputs associated with them.However, some components, in particular participants, are designed toprovide specific inputs to other components while some participantsassimilate information from other components to make inferences, detectevents and patterns and help predict, forecast or detect specificbehavioral properties of the measurements.

In an illustrative embodiment, a purpose of constructing network 100 isto modify, and if possible optimize, a value metric for which the valuenetwork is constructed. In the example of FIG. 1, a value definitiondevice 120 is used to define a value metric, which can be user-definedor system-defined. For example, in the domain of health monitoring, thevalue metric for the network may be the number of days needed todetermine when to raise an alarm about a possible outbreak of ahealth-related epidemic. Therefore, if an alarm for a possible outbreakof a particular health-related epidemic is 10 days, then a decrease inthe value metric from 10 days to 9 days is an improvement in the valuemetric. If the value metric is being optimized, then semanticmatchmaking device 135 will perform operations to reduce the number ofdays needed to raise the alarm. In the case of a business unit, thevalue metric for the network 100 may be the total sales or total profitbased on sales in a particular market or zone. In the domain of humanresource management, the value metric may be the employee retentionstatistic of a particular company.

Typically, a first step in constructing a value network 100 is toidentify and externalize a value metric. Externalize means thatassumptions (e.g., the value metric) are published so that participantsin the network 100 know what the assumptions are. The value metric couldbe a value such as 100. Alternatively, the value metric could be a rule,such as “minimize the number of days needed to determine when to raisean alarm about a possible outbreak of a health-related epidemic” or“maximize total sales.” The value definition device 120 allows a complexmetric to be used.

It should be noted that modification of the value metric may not resultin optimization of the value metric, although optimization can be aworthwhile technique. Optimization is an optional modification of thevalue metric. Optimization means that the semantic matchmaking device135 performs operations to minimize or maximize the value metric. In thecase of when the value metric corresponds to one or more values, thesemantic matchmaking device 135 optimizes the value metric by maximizingor minimizing the values associated with the value metric. If the valuemetric is a rule, there are techniques known to those skilled in the artthat can be used to determine whether a rule is optimized or notoptimized. If the rule is boolean (i.e., evaluates to true or false),optimization can be performed until the rule is true or, if possible, asclose to true as can be performed. Furthermore, there could be a casewhere optimization includes keeping the value metric stable or within acertain range.

It should also be noted that the semantic matchmaking device 135 canalso use additional metrics when modifying a value metric defined by thevalue definition device 120. For example, the consumers 110, forinstance, can output metrics that are then used by the semanticmatchmaking device 135 when the semantic matchmaking device 135 performsoperations to modify the value metric.

Components of the value network 100 include producers 105-1 and 105-2,which are responsible for producing numerical and qualitativemeasurements of the phenomena observed by these producers 105. Examplesof producers 105 include data sources such as those sources providingsales data. An exemplary producer is shown in FIG. 2. A consumer 110 ina value network accepts as input various information obtained by othercomponents of the network and analyzes the information to detectpatterns. Examples of consumers 110 include information analyzers forpurposes such as measuring anomalies. A consumer is shown in FIG. 4. Atransducer 115 in a value network 100 assimilates information of onekind and generates information of another kind. Examples of transducers115 include signal processing devices that accept inputs from producersand convert the input into processed information that can be ingested byother transducers or consumers. An exemplary transducer is shown in FIG.3.

An exemplary producer 200 is shown in FIG. 2. Exemplary producer 200 isa data source that creates an information channel 205. In informationchannel 205 can be any device or technique suitable for communicatinginformation to another device.

FIG. 3 shows an exemplary network portion 300 having a transducer 320that is coupled to M producers 305-1 through 305-M, to K normalizationinputs 325-1 through 325-K and to a consumer 330. A purpose oftransduction is to translate the information from one set of observedphenomena to another. Information can be data, knowledge, or any otheritem that can be transduced. Each producer 305 produces an associatedinformation channel 310. Transducer output 325 is meant to be consumedby other components of the network, such as consumer 330. An example ofa transducer 320 in the health monitoring scenario is a networkcomponent that accepts as input the total collection of tolls at aparticular point on a bridge and converts the total collection into thenumber of automobiles having crossed the bridge using other information,such as toll charts and camera feeds.

Additionally, a transducer 320 can use normalization inputs 325 tonormalize the information on information channels 310. For example, ifthe population that commutes daily out of a city is known, one could usethis information to help normalize relative loads at different outgoingpoints of congestion. Thus, if there are 1,000 commuters commuting outof the city and 250 commuters pass over one toll bridge, then 25 percentof the commuters pass through this point of congestion. Similarly, if ittakes 30 minutes for 250 commuters to pass over a first toll bridge, buttakes an hour for 200 commuters to pass over a second bridge, then thetransducer 320 could use normalization inputs 325 (e.g., including timeduring which commuter data was taken) to determine a “commuter rate,”which could be used to indicate that the commuter rate for the secondtoll bridge is lower than the commuter rate for the first toll bridge,even though the number of commuters passing over the second toll bridgeis smaller than the number of commuters passing over the first bridge.The consumer 330 could then use this information.

It should be noted that FIG. 3 may also be considered to be a method.The producers 305 perform the step of producing output information, thenormalization inputs 325 perform the step of producing normalizationinformation and the transducer 320 transduces and normalizes the outputinformation by using the output information and the normalizationinformation or just transduces the output information.

A network portion 400 is shown in FIG. 4. Network portion 400 contains aconsumer 420 that is interacting with two producers 410-1 and 410-2,each producing an information channel 415, and a value metric 430. Inthis example, producers 410 are shown, but one or both of the producerscould be replaced by transducers. In a health monitoring scenario,consumers 420 are typically analyzers of anomalies or patterns ofdeviation, such as devices that analyze sudden variations intemperature, and the metric 430 determined by the consumer 420 couldtherefore be related to the temperature. In an exemplary embodiment,consumers 420 in value networks consume processed or raw informationfrom other components to generate derived information pertaining toexpertise of the consumer 420. For example, a consumer 420 can bedesigned to be an expert predictor of hurricanes based on inputs likepressure, wind speeds and relative humidity, and the metric 430 couldthen be whether or not a hurricane is possible. Consumers 420 cantherefore be expert analysis tools.

In the example of FIG. 4, the metric 430 is one of several metrics (notshown in FIG. 4) that are used to help optimize the value metric. Forexample, the value metric could be the dollar amount of losses incurredthrough natural disasters in any fiscal year. In the hurricane scenarioabove, predicting the spatial and temporal location of a hurricane (asindicate by metric 430) can be helpful in minimizing the value metric.

It should be noted that FIG. 4 can also be considered to be a method.For instance, the consumer 420 can perform the step of analyzinginformation from producers 410 to produce a metric 430.

FIG. 5 shows three examples of producers in a health monitoringscenario. FIG. 5 shows exemplary heterogeneous information channels 510created by producers 505. The producers 505 include tolls collected onthe inbound George Washington bridge in New York City (producer 505-1,which produces information channel 510-1), weekly over-the-counter (OTC)drug sales for influenza in Manhattan (producer 505-2, which producesinformation channel 510-2), and daily temperature in New York City(producer 505-3, which produces information channel 505-3). Additionproducers (not shown) could include relative humidity, precipitation,and such other variables, and these variables could be used to forecastthe widespread outbreak of influenza. As another example, the OTC drugsales across all pharmacies in a city like New York can be aggregatedand classified by the age-group of the buyers. Thus, informationchannels 510 can then be used to determine whether an outbreak ofinfluenza is occurring, will occur, or has passed.

Illustratively, FIG. 6 shows an example of a consumer in a healthmonitoring scenario for a network portion 600, where the consumer is ananomaly analyzer 630. The anomaly analyzer 630 uses two sets ofinformation channels 610 from two different producers 605, which are theOTC sales information producer 605-1 and the environmental informationproducer 605-2. The anomaly analyzer 630 uses two sets of informationchannels 610 to produce an early warning alarm level 640 for outbreak ofa particular disease like influenza for the region over which theaggregated OTC sales and environmental information is made available.The early warning alarm level 640 is a metric that can be used to modifythe value metric for a value network using the network portion 600. Forexample, the value metric could be the casualty figures due to such anoutbreak.

It should be noted that FIG. 6 can also be considered to be a method.The analyzer 640 performs a step of analyzing information from theproducers 610 in order to determine a metric 650.

FIG. 7 shows an exemplary network portion 700 having a transducer 740for a health monitoring scenario. Network portion 700 includes 100pharmacy producers 710, each of which feeds OTC sales information over acorresponding one of the information channels 715 to the transducer 740,N toll bridge producers 720, each of which feeds toll collectioninformation over a corresponding one of the information channels 725 tothe transducer 740, a temperature producer 730 that feed dailytemperature information over information channel 735 to transducer 740,and transducer 740. Transducer 740 produces, as output 750, normalized,aggregated age-based and population-flow-based drug sales informationbased on data incoming from multiple heterogeneous producers 710, 720,and 730. The transducer 740 converts information from one kind (e.g.,sales, tolls, temperatures, etc.) into another kind such as aggregatednormalized inputs, which can then be used by analyzers (e.g., consumers)looking for anomalies.

FIG. 7 may also be considered to be a method, where the transducertransduces and, in this example, normalizes information from theproducers 710, 720, and 730.

The dynamic and adaptive binding of various components of the valuenetwork can be achieved, in an exemplary embodiment, through theoperation of a semantic matchmaking device 800 shown in FIG. 8. Semanticmatchmaking device 800 is an exemplary representation of the semanticmatchmaking device 135 shown in FIG. 1. In this example, the semanticmatchmaking device 800 accepts from the system or the user a valuemetric, defined by the value definition device 845, that is to bemodified. It should be noted that multiple value metrics can be used, ifdesired, but the value definition device 845 can also be adapted todefine a single value metrics for multiple value metrics. For example, avalue metric of “minimize time before an alert is issued for aninfluenza outbreak” and a value metric of “minimize casualties caused byan influenza outbreak” could be combined into a single value metric of“minimize time before an alert is issued for an influenza outbreak whileminimizing casualties caused by an influenza outbreak.”

Illustratively, the semantic matchmaking device 800 device can comprisetwo parts. One part is the messaging propagation device 850, and theother part is the semantic controller device 810. The semanticcontroller device 810 contains a semantic directory 815, a network statemonitoring device 820, a number of ontologies (e.g., such as a federatedontology) of the network components, a matchmaking analysis device 830,a value optimization device 835, and a message controller device 840.The message controller device 840 is responsible for the activity on themessaging propagation device. The semantic controller device 810 uses adefinition, provided by the value acceptance device 845, of the valuemetric to be optimized.

An exemplary operation of the semantic matchmaking device is explainedthrough the following health monitoring scenario example.

Even a simple system using the consumers and producers described inFIGS. 5 and 6 can quickly become complicated to implement in the absenceof exemplary embodiments of the present invention. First, there areseveral assumptions that can be used in a network enablinginteroperability between participants:

1) If people are exposed to rainfall or snowfall and are not properlyclad for the precipitation, they may catch a cold.

2) People buy OTC drugs when they think they are sick but do not thinkthat the sickness severity warrants a visit to their doctor.

3) If an area is affected by flu, many people in that area willeventually buy OTC drugs for the common symptoms and this will bereflected in aggregated sales over a span of few days and within closegeographical proximity.

Some of these assumptions that are made while designing a networkenabling interoperability between participants may seem trivial butundoubtedly illustrate the kind of domain knowledge that can go into thedesign of components of the network. Similarly, the fact that OTC drugsales are designed to be transduced by transducers or produced bymeasurement devices also reflects domain knowledge. The output producedby the transducers or producers is interpreted based on knowledge. Forexample, the alarm level being high in winter is interpreted differentlyfrom the alarm level being high in summer or spring. Thus, there is asignificant semantic component to the design, analysis andinterpretation of transducers, producers and consumers. Current state ofthe art techniques do not however support any abstraction of thissemantics. Thus, even if an abstraction of semantics is created, currenttechniques would not use the semantics. However, exemplary embodimentsof the present invention can use the semantics when enablinginteroperability between participants in a value network.

Examples are now listed of the tasks that one might expect to be used ina value network even in the case of the simplistic system of FIG. 1.

1) Some channels provide temperature in degrees Celsius while othersprovide them in degrees Fahrenheit. The feature extraction performed byexemplar embodiments of the present invention should be able to dealwith the fact that temperature can be measured in different scales.

2) People buy some OTC drugs when there is a sale or when they know thata particular drug is not easily available and often is in short supplybut is routinely needed. The feature extraction performed by exemplaryembodiments of the present invention should thus be able to discount theeffect of sales related to such drugs.

3) There is seasonality in many outbreaks such as the flu. Thus, highalarm levels during other seasons should quickly trigger alerts.

4) If a particular drug is replaced by a pharmaceutical company byanother drug, the system should be able to determine this informationand reconfigure itself.

Components of a value network should thus be able to abstract componentrequirements, assumptions and outputs so that efficient messagingpropagation analysis and value optimization may be performed. Forexample, if it is raining and a work-site is being monitored, andassuming that the appropriate visual sensors are in place, the valuenetwork may want to know the number of people who walked in with anumbrella or wearing a raincoat. If this number is low and a pharmacy inthe neighborhood, then communicates a sudden increase in the sale of OTCdrugs for colds, the system should be able to relate the two events.Thus, the analyzer (e.g., a consumer) should be able to describe thesemantics that the analyzer wants to analyze and not have to define theexact format and channel of the input information.

Enabling such and other types of interoperability is performed in anexemplary embodiment by the semantic matchmaking device 800. Thesemantic controller device 810, as part of the semantic matchmakingdevice 800, includes a semantic directory 815 that has semantics forcomponents in a value network, such as the participants of consumers,producers, and transducers. Semantics for a consumer, for example, woulddefine in the OTC medication example given above that certain sales arefor medicines of a particular upper respiratory type of affliction.Thus, changes in brand names would not alter the count given by theproducer of the medicines sold. Normalization could be performed by, fora given drug store, if the population within a certain area around thedrug store is known, then the population could be used to determine thepercentage of the population buying drugs for the upper respiratory typeof affliction. The semantic directory 815 also defines interactionsbetween the participants and defines the information produced or to beconsumed by the participants. Interactions include data flow betweenparticipants. The message controller device 840 permits flow betweenparticipants and directs the flow, but at the same time the permissionand direction of flow is based on the semantic definitions of inputs,outputs, and functionality of the participants (e.g., or othercomponents) as defined in the semantic directory 815.

The semantic controller device 810 also includes one or more ontologies825, such as a federated ontology, that help to provide appropriate andrelevant inputs to components and to interpret the produced outputs ofthe components.

The semantic controller device 810 additionally includes a valueoptimization device 830 that optimizes the value metric of the networkin conjunction with the network state device and the other components ofthe semantic matchmaking device. The value definition device 845 definesthe value metric. Finally, the semantic matchmaking device 800 containsthe messaging propagation device 850 that controls the flow ofinformation in the messaging propagation device 850. It is through themessaging controller device 850 that the semantic controller device 810effects and directs efficient adaptive flow of information in the valuenetwork to achieve an optimal value of the value metric or to modify avalue of the value metric. The semantic controller device 810 uses oneor more control signals 851 and one or more feedback signals 852 whencontrolling the messaging propagation device 850. The linkages 860 aredirected by the semantic controller device 810 and the semanticcontroller device 810 makes these linkages 860 dynamic. In an exemplaryembodiment, the semantic matchmaking device 800 directs and providesconnectivity based generally on a high-level semantic analysis of thecomponents of the value network and the optimization criteria drivingthe interconnection is the value metric used to determine value for thenetwork.

The matchmaking analysis device 830 drives information flow betweenproducers, consumers, and transducers that are semantically related. Thematchmaking analysis device 830 will link participants the matchmakinganalysis device 830 determines will lead to the modification of thevalue metric defined by the value definition device 845. It should benoted that the matchmaking analysis device 830 could link twoparticipants (for instance), then determine over some period of timethat the linkage in information flow between the two participantsworsens the value metric and therefore the matchmaking analysis device830 could unlink the two participants.

In addition to adaptive and directed information flow, the semanticmatchmaking device 800 also can control the adaptation of components ofthe network. For example, based on newly supplied information, thesemantic matchmaking device 800 can send a request to a networkcomponent to adapt itself as shown in FIG. 9. FIG. 9 shows theadaptation of a consumer based on supervision through manually labeledexemplars. FIG. 9 shows an exemplary network portion 900 comprising thesemantic controller device 810, a producer 910 that produces informationon information channel 915, a consumer 920 that is coupled to theinformation channel 915 and that produces a performance measurementmetric 930, and training exemplars 940. The semantic controller device810 causes the training exemplars to be directed to the consumer 920 byusing the information channel 945 (as set up by the messagingpropagation device 850, for instance, which is not shown in FIG. 9). Thetraining exemplars 920 then cause behavior of the consumer 920 tochange. For instance, the training exemplars 940 could show that thenumber of OTC drugs purchased in a two week time period is lower thanoriginally determined, and the consumer 920 will modify calculationsused to determine the performance measurement metric 930 in light of thetraining exemplars.

Another technique for changing the behavior of the consumer 920 is byhaving the semantic controller device 810 perform a statistical analysisof the current behavior of the consumer 920 with respect to participantswithin a predetermined distance from the consumer 920 and that arereachable by information channels from the consumer 920. For instance,if other participants experience errors and the errors can bestatistically determined to be caused by the consumer 920, then thesemantic controller device 810 can modify the behavior of the consumer920 by providing additional information (in the form of trainingexemplars 940 or other information) to the consumer 920 to change thebehavior of the consumer 920. The statistical analysis could bedetermined by determining how many participants have errors, how muchdata is being transferred from or to the consumer 920, and how often theconsumer 920 provides data over information channels. Any statisticsassociated with the component (e.g., consumer 920) can be used.

Of the adaptations that can result in dynamic alignment of variouscomponents of the network, the most typical changes arise due totemporary or permanent emergence or disappearance of producers andconsumers. A network portion 1000 is shown in FIG. 10 and the networkportion includes two producers 1010 producing two information channels1015 that are coupled to a matchmaking analysis device 1030, thematchmaking analysis device 1030, a semantic directory 1020, and aperformance measurement metric 1040. The semantic directory 1020 is anexample of the semantic directory 815 of FIG. 8, and the matchmakinganalysis device 1030 is an example of a matchmaking analysis device 830of FIG. 8.

FIG. 10 also shows an exemplary method by which the matchmaking analysisdevice 830 in a semantic controller device (e.g., semantic controllerdevice 810) can automatically model relationships between differentcomponents so that the semantic controller device can be used to alignone or more of the components. For example, the matchmaking analysisdevice 1030 can automatically learn, through techniques known to thoseskilled in the art, that producers 1010-1 and 1010-2 are highlycorrelated and that one producer can be replaced by another in theabsence of one of them.

The performance measurement metric 1040 is used when modifying the valuemetric of the value network. However, the relationship between theperformance measurement metric 1040 and the value metric may not be asimple relationship. As an illustration, the value metric might bemaximizing revenues or minimizing casualties. The value metric in turncan be converted into numerous low level metrics, like “meet targetednumber of consumers,” “do not exceed targeted levels of inventory,” or“have a precision of over 90 percent in detecting a terror attack.” Thelow level metrics, such as performance measurement metric 1040 can beused by the value optimization device 835 (see FIG. 8) when the semanticcontroller device 810 performs operations to modify the value metric.

Illustratively, the matchmaking analysis device 1030 uses semanticmodels in the semantic directory 1020 and the performance measurementmetric 1040 when learning whether the producers 1010-1 and 1010-2 arecorrelated. For instance, if the performance measurement metric 1040 isbenefited by using solely producer 1010-1 or by using solely producer1010-2 and the benefit incurred is about equal for each producer 1010,then the matchmaking analysis device 1030 could substitute one producer1010 for another (e.g., producer 1010-2 could be used instead ofproducer 1010-1). However, the matchmaking analysis device 1030 may alsodetermine that using information channels 1015 from both producerstogether create a benefit for the performance measurement metric 1040.For instance, if it is winter as determined by producer 1010-1 and it iscold as determined by producer 1010-2, the metric of “possibility of aflu outbreak” might be higher if it is both winter and cold, than if itis winter but not cold or if it is cold but not winter.

FIGS. 11, 12, and 13 illustrate adaptive control of information flow inorder to enable interoperability between participants in a network.

FIG. 11 shows a snapshot of the adaptive matchmaking of producers 1120-1through 1120-M and consumers 1140-1 through 1140-N at a particular statein time. In network portion 1100, the semantic controller device 1110has directed the messaging propagation device 1130 to set up informationflow as shown in FIG. 11. Linkages 1135 are dynamic and are betweenproducers 1120 and consumers 1140 at the current state. In this state,the consumer 1140-N is linked with producers 1120-1, 1120-2 and 1120-M.Meanwhile, consumer 1140-1 is linked to producer 1120-1 and consumer1140-2 is linked to producer 1120-2.

Now assume that producer 1120-2 drops out such as by being unresponsiveor sending a “removal” message. FIG. 12 shows another snapshot of thenetwork portion 1100 at a different state in time. Dashed connectionsrepresent the dynamic linkage between producers and consumers within thestate. Notice that the producer 1120-2 is missing (as shown by a dashedline on producer 1120-2) in this state and hence the linkages 1135-3 and1135-4 are deleted. Instead, consumer 1140-2 is now linked to producer1120-1 through linkage 1135-6. This can for example be based on analysisas shown in and described in reference to FIG. 10 that results in theidentification that producer 1120-1 is the most highly correlatedproducer 1120 to producer 1120-2 and hence can be used in place ofproducer 1120-2. Linkages 1135 are created, maintained, deleted, andreplaced based on semantic models for the producers 1120 and theconsumers 1140, where the semantic models are typically listed in asemantic directory (not shown in FIG. 12). Note that the semanticcontroller device 1110 could determine that a producer or otherparticipant should be deleted from the network and therefore can beremoved from the network. For instance, a producer could be redundant(for instance, produces results that are fungible with other results) ora transducer could produce erroneous or invalid results.

FIG. 13 offers another snapshot of the same network portion 1100 at adifferent state in time. Notice that a new producer 1320 is nowavailable. Sometime after the new producer 1320 becomes available andlists itself in the semantic directory (not shown in FIG. 13), thesemantic model for the new producer 1320 is analyzed by the semanticcontroller device 1110 and the information (typically an informationchannel) from the new producer 1320 is appropriately linked to consumer1140-2 through linkage 1135-7.

The determination to link the new producer 1320 to the consumer 1140-2can be based, for example, on the information that the new producer 1320lists in the semantic directory. For instance, producer 1120-2 may haveproduced information of the number of fever reduction capsules forchildren sold over the counter in New York City. The new producer 1320that is now used to replace the producer 1120-2 may be the number ofelementary school absentees in New York City reportedly absent due toflu. Since both producers 1320, 1120-2 have listed (e.g., through asemantic model) their abstract information type in the semanticdirectory, it is possible for the matchmaking analysis device of thesemantic controller device 1110 to determine that information from thenew producer 1320 can be used by the consumer 1140-2, and the semanticcontroller device 1110 will direct the messaging propagation device 1130to create linkage 1135-7 in order to route the information from the newproducer 1320 to the consumer 1140-2.

Turning now to FIG. 14, a computer system 1400 is shown that is suitablefor implementing one or more components of a value network, such as asemantic matchmaking device. Computer system 1400 includes a processor1410 that is singular or distributed, a memory 1420 that is singular ordistributed, a network interface 1430, and a media interface 1440. Thenetwork interface 1430 couples to a network 1470 (e.g., portions of avalue network). The media interface 1440 couples to a computer readablemedium 1460, which includes one or more programs (not shown) which, whenimplemented, carry out portions or all of the present invention. Forexample, the semantic matchmaking device 800 can be included as aprogram. The program, when loaded into processor 1410 (typically frommemory 1420), will configure the processor 1410 to implement thesemantic matchmaking device 800.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of thisinvention and that various modifications may be implemented by thoseskilled in the art without departing from the scope and spirit of theinvention.

What is claimed is:
 1. A method for enabling interoperability between aplurality of participants in a network, the method comprising the stepsof: determining a value associated with a value metric defined for atleast a portion of the network; determining two or more alternativeinformation flows between two or more of the plurality of participantsin the network based at least in part on one or more semantic modelscorresponding to the plurality of participants and on the valueassociated with the value metric, wherein the one or more semanticmodels define inputs, outputs, and functionality for each of theplurality of participants and define at least a portion of informationproduced or consumed by the plurality of participants; and optimizing anoverall value of the network by selecting combinations of saidalternative information flows for at least one portion of the network inorder to satisfy consumers of information, wherein one or more steps areperformed by a processor.
 2. The method of claim 1, wherein the step ofdetermining two or more alternative information flows further comprisesthe steps of: performing matchmaking between the plurality ofparticipants based on the one or more semantic models and the value; anddirecting information flow between the participants matched during thematchmaking.
 3. The method of claim 2, wherein the step of matchmakingcomprises the steps of: determining by using the one or more semanticmodels that the participants can be connected; and determining that thevalue metric is modified by the connection.
 4. The method of claim 1,further comprising the step of normalizing the portion of informationbased on the one or more semantic models, and wherein the step ofdetermining two or more alternative information flows uses thenormalized portion of information.
 5. The method of claim 1, furthercomprising the step of performing the determining steps multiple timesin order to modify the value associated with the value metric.
 6. Themethod of claim 1, wherein the steps of determining are completed anumber of times in order to modify the value associated with the valuemetric from one or more initial values to one or more final values,wherein each of the number of times the steps of determining arecompleted creates one or more intermediate values associated with thevalue metric, and wherein the one or more final values are selected whenchanges in directing information between two or more of the plurality ofparticipants cause currently determined one or more intermediate valuesto be within a predetermined distance from previously determined one ormore intermediate values.
 7. The method of claim 1, further comprisingthe step of generating one or more alerts based on the value of thevalue metric.
 8. The method of claim 1, wherein the step of determiningtwo or more alternative information flows further comprises the stepsof: inserting a new participant into the network based on a semanticmodel of the new participant; and directing information flow between oneor more existing participants and the new participant.
 9. The method ofclaim 1, wherein a given participant has information flow coupled to oneor more particular participants, and wherein the step of determining twoor more alternative information flows further comprises the steps of:determining that the given participant has been removed from thenetwork; and directing information, by using the one or more semanticmodels, from one or more existing participants to the one or moreparticular participants.
 10. The method of claim 9, wherein the step ofdetermining two or more alternative information flows further comprisesthe steps of determining that the given participant can be deleted fromthe network and deleting the given participant from the network.
 11. Themethod of claim 1, wherein the step of determining two or morealternative information flows comprises the step of directing newinformation to a given participant, whereby behavior of the givenparticipant can change in response to the new information.
 12. Themethod of claim 11, wherein the step of determining two or morealternative information flows further comprises the step of performingan analysis of statistical behavior of the given participant withrespect to participants within a predetermined distance in the networkfrom the given participant.
 13. The method of claim 1, wherein the stepof determining two or more alternative information flows uses a semanticdirectory to access the one or more semantic models.
 14. The method ofclaim 13, wherein the semantic directory lists one or more of services,assumptions and processing methodologies of the plurality ofparticipants.
 15. The method of claim 14, further comprising the step ofexternalizing the services, assumptions, and processing methodologies ofthe plurality of participants, wherein the externalization is performedusing one or more of specifically designed ontologies and federatedontologies.
 16. The method of claim 1, wherein the participants of thenetwork comprise producers of information, consumers of information, andtransducers of information.
 17. The method of claim 16, wherein thetransducers convert information from one kind to another kind.
 18. Themethod of claim 1, wherein one or more of the participants produce a lowlevel metric, and wherein the step of determining a value uses the lowlevel metric.
 19. The method of claim 1, wherein the value metriccomprises one or more of a rule, an equation that evaluates to a value,and a boolean rule.
 20. An apparatus for enabling interoperabilitybetween a plurality of participants in a network, the apparatuscomprising: one or more memories; and one or more processors coupled tothe one or more memories, the one or more processors configured: todetermine a value associated with a value metric defined for at least aportion of the network; to determine two or more alternative informationflows between two or more of the plurality of participants in thenetwork based at least in part on one or more semantic modelscorresponding to the plurality of participants and on the valueassociated with the value metric, wherein the one or more semanticmodels define inputs, outputs, and functionality for each of theplurality of participants and define at least a portion of informationproduced or consumed by the plurality of participants; and to optimizean overall value of the network by selecting combinations of saidalternative information flows for at least one portion of the network inorder to satisfy consumers of information.
 21. The apparatus of claim20, wherein the one or more processors are further configured, whendirecting information flow: to perform matchmaking between the pluralityof participants based on the one or more semantic models and the one ormore values; and to directing information flow-between the participantsmatched during the matchmaking.
 22. The apparatus of claim 20, whereinthe one or more processors are further configured to perform thedetermining steps multiple times in order to modify the value associatedwith the value metric.
 23. The apparatus of claim 20, wherein thedetermining operations are completed a number of times in order tomodify the value associated with the value metric from one or moreinitial values to one or more final values, wherein each of the numberof times the determining operations are completed creates one or moreintermediate values associated with the value metric, and wherein theone or more final values are selected when changes in directinginformation between two or more of the plurality of participants causecurrently determined one or more intermediate values to be within apredetermined distance from previously determined one or moreintermediate values.
 24. The apparatus of claim 20, wherein the one ormore processors are further configured to generate one or more alertsbased on the value of the value metric.
 25. The apparatus of claim 20,wherein the one or more processors are further configured, whendirecting information flow: to insert a new participant into the networkbased on a semantic model of the new participant; and to directinformation flow between one or more existing participants and the newparticipant.
 26. The apparatus of claim 20, wherein a given participanthas information flow coupled to one or more particular participants, andwherein the one or more processors are further configured, whendirecting information flow: to determine that the given participant hasbeen removed from the network; and to direct information, by using theone or more semantic models, from one or more existing participants tothe one or more particular participants.
 27. The apparatus of claim 20,wherein the one or more processors are further configured, whendirecting information flow, to direct new information to a givenparticipant, whereby behavior of the given participant can change inresponse to the new information.
 28. The apparatus of claim 20, whereinthe one or more processors are further configured, when directinginformation flow, to use a semantic directory to access the one or moresemantic models.
 29. The apparatus of claim 20, wherein the participantsof the network comprise producers of information, consumers ofinformation, and transducers of information.
 30. The apparatus of claim20, wherein one or more of the participants produce a low level metric,and wherein the one or more processors are further configured, whendetermining the value, to use the low level metric when determining thevalue.
 31. The apparatus of claim 20, wherein the value metric comprisesone or more of a rule, an equation that evaluates to a value, and aboolean rule.
 32. An article of manufacture for enablinginteroperability between a plurality of participants in a network, thearticle of manufacture comprising: a non-transitory computer readablerecordable medium containing one or more programs which when executedimplement the steps of: determining a value associated with a valuemetric defined for at least a portion of the network; determining two ormore alternative information flows between two or more of the pluralityof participants in the network based at least in part on one or moresemantic models corresponding to the plurality of participants and onthe value associated with the value metric, wherein the one or moresemantic models define inputs, outputs, and functionality for each ofthe plurality of participants and define at least a portion ofinformation produced or consumed by the plurality of participants; andoptimizing an overall value of the network by selecting combinations ofsaid alternative information flows for at least one portion of thenetwork in order to satisfy consumers of information.